Fast Search for Dirichlet Process Mixture Models
Abstract
Dirichlet process (DP) mixture models provide a flexible Bayesian framework for density estimation. Unfortunately, their flexibility comes at a cost: inference in DP mixture models is computationally expensive, even when conjugate distributions are used. In the common case when one seeks only a maximum a posteriori assignment of data points to clusters, we show that search algorithms provide a practical alternative to expensive MCMC and variational techniques. When a true posterior sample is desired, the solution found by search can serve as a good initializer for MCMC. Experimental results show that using these techniques is it possible to apply DP mixture models to very large data sets.
Cite
Text
Iii. "Fast Search for Dirichlet Process Mixture Models." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Iii. "Fast Search for Dirichlet Process Mixture Models." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/iii2007aistats-fast/)BibTeX
@inproceedings{iii2007aistats-fast,
title = {{Fast Search for Dirichlet Process Mixture Models}},
author = {Iii, Hal Daume},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {83-90},
volume = {2},
url = {https://mlanthology.org/aistats/2007/iii2007aistats-fast/}
}